{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Unpaired Style Transfer using CycleGAN\n",
        "[CycleGAN](https://arxiv.org/abs/1611.07004) improves upon paired style transfer architecture by relaxing the constraint on input and output images. CycleGAN explores the unpaired style transfer paradigm where the model actually tries to learn the stylistic differences between source and target domains without explicit pairing between input and output images. Zhu and Park et al. describe this unpaired style transfer similar to our ability of imagining how a Van Gogh or Monet would have painted a particular scene (without having actually seen a side by side example). Quoting from the paper itself,\n",
        "\n",
        "> Instead, we have knowledge of the set of Monet paintings and of the set of landscape photographs. We can reason about the stylistic differences between these two sets, and thereby imagine what a scene might look like if we were to “translate” it from one set into the other.\n",
        "\n",
        "This provides a nice advantage as well as opens additional use cases where exact pairing of source and target domains is either not available or we do not have enough training examples.\n",
        "\n"
      ],
      "metadata": {
        "id": "mEWl8xxU3sKu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Libraries"
      ],
      "metadata": {
        "id": "N6FXnJHk32ND"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "9KlERwzUJbNr"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import itertools\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets\n",
        "from torch.autograd import Variable\n",
        "from torch.utils.data import Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision.utils import save_image, make_grid\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision.datasets.utils import download_and_extract_archive\n",
        "\n",
        "import os\n",
        "import glob\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## U-Net Generator\n",
        "The U-Net architecture uses skip connections to shuttle important features between the input and outputs. In case of pix2pix GAN, skip connections are added between every $ith$ down-sampling and $(n-i)th$ over-sampling layers, where $n$ is the total number of layers in the generator. The skip connection leads to concatenation of all channels from the ith and $(n-i)th$ layers."
      ],
      "metadata": {
        "id": "E5hkGY1V4OG8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class UpSampleBlock(nn.Module):\n",
        "    def __init__(self, input_channels, output_channels):\n",
        "        super(UpSampleBlock, self).__init__()\n",
        "        layers = [\n",
        "            nn.ConvTranspose2d(\n",
        "                input_channels,\n",
        "                output_channels,\n",
        "                kernel_size=4,\n",
        "                stride=2,\n",
        "                padding=1,\n",
        "                bias=False),\n",
        "        ]\n",
        "        layers.append(nn.InstanceNorm2d(output_channels))\n",
        "        layers.append(nn.ReLU(inplace=True))\n",
        "        layers.append(nn.Dropout(0.5))\n",
        "        self.model = nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x, skip_connection):\n",
        "        x = self.model(x)\n",
        "        x = torch.cat((x, skip_connection), 1)\n",
        "\n",
        "        return x"
      ],
      "metadata": {
        "id": "X0FhVXlZJpu6"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class DownSampleBlock(nn.Module):\n",
        "    def __init__(self, input_channels, output_channels,normalize=True):\n",
        "        super(DownSampleBlock, self).__init__()\n",
        "        layers = [\n",
        "            nn.Conv2d(\n",
        "                input_channels,\n",
        "                output_channels,\n",
        "                kernel_size=4,\n",
        "                stride=2,\n",
        "                padding=1,\n",
        "                bias=False)\n",
        "            ]\n",
        "        if normalize:\n",
        "          layers.append(nn.InstanceNorm2d(output_channels))\n",
        "        layers.append(nn.LeakyReLU(0.2))\n",
        "        layers.append(nn.Dropout(0.5))\n",
        "        self.model = nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)"
      ],
      "metadata": {
        "id": "S4qty9n1Jpxn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Generator(nn.Module):\n",
        "    def __init__(self, input_channels=3,out_channels=3):\n",
        "        super(Generator, self).__init__()\n",
        "        self.downsample1 = DownSampleBlock(input_channels,64, normalize=False)\n",
        "        self.downsample2 = DownSampleBlock(64, 128)\n",
        "        self.downsample3 = DownSampleBlock(128, 256)\n",
        "        self.downsample4 = DownSampleBlock(256, 512)\n",
        "        self.downsample5 = DownSampleBlock(512, 512)\n",
        "        self.downsample6 = DownSampleBlock(512, 512)\n",
        "        self.downsample7 = DownSampleBlock(512, 512)\n",
        "        self.downsample8 = DownSampleBlock(512, 512,normalize=False)\n",
        "\n",
        "        self.upsample1 = UpSampleBlock(512, 512)\n",
        "        self.upsample2 = UpSampleBlock(1024, 512)\n",
        "        self.upsample3 = UpSampleBlock(1024, 512)\n",
        "        self.upsample4 = UpSampleBlock(1024, 512)\n",
        "        self.upsample5 = UpSampleBlock(1024, 256)\n",
        "        self.upsample6 = UpSampleBlock(512, 128)\n",
        "        self.upsample7 = UpSampleBlock(256, 64)\n",
        "\n",
        "        self.final_layer = nn.Sequential(\n",
        "            nn.Upsample(scale_factor=2),\n",
        "            # padding left, right, top, bottom\n",
        "            nn.ZeroPad2d((1, 0, 1, 0)),\n",
        "            nn.Conv2d(128, out_channels, 4, padding=1),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        # downsampling blocks\n",
        "        d1 = self.downsample1(x)\n",
        "        d2 = self.downsample2(d1)\n",
        "        d3 = self.downsample3(d2)\n",
        "        d4 = self.downsample4(d3)\n",
        "        d5 = self.downsample5(d4)\n",
        "        d6 = self.downsample6(d5)\n",
        "        d7 = self.downsample7(d6)\n",
        "        d8 = self.downsample8(d7)\n",
        "        # upsampling blocks with skip connections\n",
        "        u1 = self.upsample1(d8, d7)\n",
        "        u2 = self.upsample2(u1, d6)\n",
        "        u3 = self.upsample3(u2, d5)\n",
        "        u4 = self.upsample4(u3, d4)\n",
        "        u5 = self.upsample5(u4, d3)\n",
        "        u6 = self.upsample6(u5, d2)\n",
        "        u7 = self.upsample7(u6, d1)\n",
        "\n",
        "        return self.final_layer(u7)"
      ],
      "metadata": {
        "id": "nbjEnhYGJpsB"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Patch-GAN Discriminator\n",
        "The authors for pix2pix propose a Patch-GAN setup for the discriminator which takes the required inputs and generates an output of size NxN. Each $x_{ij}$ element of the NxN output signifies whether the corresponding patch ij in the generated image is real or fake. Each output patch can be traced back to its initial input patch basis the effective receptive field for each of the layers."
      ],
      "metadata": {
        "id": "d5gQ7yrG4Uzp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Discriminator(nn.Module):\n",
        "    def __init__(self, input_channels=3):\n",
        "        super(Discriminator, self).__init__()\n",
        "\n",
        "        def discriminator_block(input_filters, output_filters):\n",
        "            layers = [\n",
        "                nn.Conv2d(\n",
        "                    input_filters,\n",
        "                    output_filters,\n",
        "                    kernel_size=4,\n",
        "                    stride=2,\n",
        "                    padding=1)\n",
        "                ]\n",
        "            layers.append(nn.InstanceNorm2d(output_filters))\n",
        "            layers.append(nn.LeakyReLU(0.2, inplace=True))\n",
        "            return layers\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            *discriminator_block(input_channels, output_filters=64),\n",
        "            *discriminator_block(64, 128),\n",
        "            *discriminator_block(128, 256),\n",
        "            *discriminator_block(256, 512),\n",
        "            # padding left, right, top, bottom\n",
        "            nn.ZeroPad2d((1, 0, 1, 0)),\n",
        "            nn.Conv2d(512, 1, 4, padding=1, bias=False)\n",
        "        )\n",
        "\n",
        "    def forward(self, img):\n",
        "        return self.model(img)"
      ],
      "metadata": {
        "id": "TX2unZt1JppL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Dataset Class"
      ],
      "metadata": {
        "id": "i91ulRNU44HK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ImageDataset(Dataset):\n",
        "    def __init__(self, dataset_path, is_train=False, image_transformations=None):\n",
        "        self.transform = transforms.Compose(image_transformations)\n",
        "        if is_train:\n",
        "          dataset_path += '/train'\n",
        "        else:\n",
        "          dataset_path += '/test'\n",
        "        self.files_a = sorted(glob.glob(os.path.join(dataset_path) + \"A/*.*\"))\n",
        "        self.files_b = sorted(glob.glob(os.path.join(dataset_path) + \"B/*.*\"))\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "\n",
        "        img_a = Image.open(self.files_a[index % len(self.files_a)])\n",
        "        img_b = Image.open(self.files_b[index % len(self.files_b)])\n",
        "\n",
        "        img_a = self.transform(img_a)\n",
        "        img_b = self.transform(img_b)\n",
        "\n",
        "        return {\"A\": img_a, \"B\": img_b}\n",
        "\n",
        "    def __len__(self):\n",
        "        return max(len(self.files_a), len(self.files_b))"
      ],
      "metadata": {
        "id": "Anu2QD74Pw3L"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def sample_images(batches_done):\n",
        "    imgs = next(iter(val_dataloader))\n",
        "    generator_AB.eval()\n",
        "    generator_BA.eval()\n",
        "    # real A-> B\n",
        "    real_A = Variable(imgs[\"A\"].type(Tensor))\n",
        "    # generated A-> B\n",
        "    fake_B = generator_AB(real_A)\n",
        "    # real B-> A\n",
        "    real_B = Variable(imgs[\"B\"].type(Tensor))\n",
        "    # generated B-> A\n",
        "    fake_A = generator_BA(real_B)\n",
        "\n",
        "    # reconstruct orginal samples from both generators\n",
        "    reconstruct_A = generator_BA(fake_B)\n",
        "    reconstruct_B = generator_AB(fake_A)\n",
        "\n",
        "    real_A = make_grid(real_A, nrow=5, normalize=True)\n",
        "    real_B = make_grid(real_B, nrow=5, normalize=True)\n",
        "    fake_A = make_grid(fake_A, nrow=5, normalize=True)\n",
        "    fake_B = make_grid(fake_B, nrow=5, normalize=True)\n",
        "    reconstruct_A = make_grid(reconstruct_A, nrow=5, normalize=True)\n",
        "    reconstruct_B = make_grid(reconstruct_B, nrow=5, normalize=True)\n",
        "\n",
        "    img_sample = torch.cat((real_A, fake_B, reconstruct_A,real_B, fake_A,reconstruct_B), 1)\n",
        "    save_image(img_sample, f\"images/{DATASET}_b_{batches_done}.png\", nrow=5, normalize=False)"
      ],
      "metadata": {
        "id": "Dk0_E8SXV3xB"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download Dataset"
      ],
      "metadata": {
        "id": "2bTJhEZG5brD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set experiment by chosing dataset as\n",
        "# 1. apple2orange or\n",
        "# 2. vangogh2photo\n",
        "DATASET = 'vangogh2photo'\n",
        "DATASET_PATH = f\"./images/{DATASET}\""
      ],
      "metadata": {
        "id": "kRiE5cvOXHsR"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.makedirs(f\"{DATASET_PATH}\", exist_ok=True)\n",
        "os.makedirs(\"saved_models/\", exist_ok=True)"
      ],
      "metadata": {
        "id": "YAHrHz84RlJU"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -P images/ https://efrosgans.eecs.berkeley.edu/cyclegan/datasets/{DATASET}.zip\n",
        "!unzip {DATASET_PATH}.zip -d ./images/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8uS_P5PUDwA",
        "outputId": "689584a3-95da-44cb-e25e-d083b6480638"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set Parameters"
      ],
      "metadata": {
        "id": "_T7bKz_T5EP4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "IMG_WIDTH = 256\n",
        "IMG_HEIGHT = 256\n",
        "NUM_CHANNELS = 3\n",
        "BATCH_SIZE = 16\n",
        "N_EPOCHS = 200\n",
        "SAMPLE_INTERVAL = 18"
      ],
      "metadata": {
        "id": "O1qyHFhZOvJz"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CUDA = True if torch.cuda.is_available() else False"
      ],
      "metadata": {
        "id": "RqxpSPgrPw0h"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate Receptive Field for Patch"
      ],
      "metadata": {
        "id": "6Jtbrajj5MlI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prepare patch size for our setup\n",
        "patch = int(IMG_HEIGHT / 2**4)\n",
        "patch_gan_shape = (1,patch, patch)\n",
        "print(\"Patch Shape={}\".format(patch_gan_shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yWfLxI-DRlCe",
        "outputId": "22912628-30b6-461f-ced8-4117a86d9ce9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patch Shape=(1, 16, 16)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get Generator and Discriminator Model Objects"
      ],
      "metadata": {
        "id": "FbXMgH8_5Pyp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize generator and discriminator\n",
        "generator_AB = Generator()\n",
        "generator_BA = Generator()\n",
        "discriminator_A = Discriminator()\n",
        "discriminator_B = Discriminator()\n",
        "\n",
        "\n",
        "# Loss functions\n",
        "adversarial_loss = torch.nn.MSELoss()\n",
        "cycle_loss = torch.nn.L1Loss()\n",
        "identity_loss = torch.nn.L1Loss()\n",
        "\n",
        "# Loss weight of L1 pixel-wise loss between translated image and real image\n",
        "weight_pixel_wise_identity = 100\n",
        "\n",
        "# Optimizers\n",
        "optimizer_G = torch.optim.Adam(\n",
        "    itertools.chain(generator_AB.parameters(), generator_BA.parameters()), lr=0.0002, betas=(0.5, 0.999)\n",
        "    )\n",
        "optimizer_D_A = torch.optim.Adam(discriminator_A.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
        "optimizer_D_B = torch.optim.Adam(discriminator_B.parameters(), lr=0.0002, betas=(0.5, 0.999))"
      ],
      "metadata": {
        "id": "CFJbgyHeRlFh"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if CUDA:\n",
        "    generator_AB = generator_AB.cuda()\n",
        "    generator_BA = generator_BA.cuda()\n",
        "    discriminator_A = discriminator_A.cuda()\n",
        "    discriminator_B = discriminator_B.cuda()\n",
        "    adversarial_loss.cuda()\n",
        "    cycle_loss.cuda()\n",
        "    identity_loss.cuda()\n",
        "    Tensor = torch.cuda.FloatTensor\n",
        "else:\n",
        "  Tensor = torch.FloatTensor"
      ],
      "metadata": {
        "id": "6dr3Jr7ySNJ4"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define Transformations and Dataloaders"
      ],
      "metadata": {
        "id": "VvRfehAN5o8I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image_transformations = [\n",
        "    transforms.Resize(int(IMG_HEIGHT * 1.12), Image.BICUBIC),\n",
        "    transforms.RandomCrop((IMG_HEIGHT, IMG_WIDTH)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "]"
      ],
      "metadata": {
        "id": "DjbEdO81SNHa"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = DataLoader(\n",
        "    ImageDataset(DATASET_PATH, is_train=True,image_transformations=image_transformations),\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True\n",
        ")"
      ],
      "metadata": {
        "id": "lv0x9dX_SNEs"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_dataloader = DataLoader(\n",
        "    ImageDataset(DATASET_PATH,image_transformations=image_transformations),\n",
        "    batch_size=5,\n",
        "    shuffle=True\n",
        ")"
      ],
      "metadata": {
        "id": "3cA2eGNXSNBz"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Begins!"
      ],
      "metadata": {
        "id": "sfCtlMsj5tTV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(0, N_EPOCHS):\n",
        "    for i, batch in enumerate(train_dataloader):\n",
        "\n",
        "        # prepare inputs\n",
        "        real_A = Variable(batch[\"A\"].type(Tensor))\n",
        "        real_B = Variable(batch[\"B\"].type(Tensor))\n",
        "\n",
        "        # ground truth\n",
        "        valid = Variable(Tensor(np.ones((real_A.size(0), *patch_gan_shape))), requires_grad=False)\n",
        "        fake = Variable(Tensor(np.zeros((real_A.size(0), *patch_gan_shape))), requires_grad=False)\n",
        "\n",
        "        #  Train Generator\n",
        "        generator_AB.train()\n",
        "        generator_BA.train()\n",
        "        optimizer_G.zero_grad()\n",
        "\n",
        "        # identity loss\n",
        "        idn_loss_A = identity_loss(generator_AB(real_A), real_A)\n",
        "        idn_loss_B = identity_loss(generator_BA(real_B), real_B)\n",
        "\n",
        "        idn_loss = (idn_loss_A + idn_loss_B) / 2\n",
        "\n",
        "\n",
        "        # generator loss\n",
        "        fake_B = generator_AB(real_A)\n",
        "        pred_fake = discriminator_B(fake_B)\n",
        "        adv_loss_AB = adversarial_loss(pred_fake, valid)\n",
        "\n",
        "        fake_A = generator_BA(real_B)\n",
        "        pred_fake = discriminator_A(fake_A)\n",
        "        adv_loss_BA = adversarial_loss(pred_fake, valid)\n",
        "\n",
        "\n",
        "        # GAN loss\n",
        "        adv_loss = (adv_loss_AB + adv_loss_BA)/2\n",
        "\n",
        "        # Cycle loss\n",
        "        reconstruction_A = generator_BA(fake_B)\n",
        "        cycle_loss_A = cycle_loss(reconstruction_A, real_A)\n",
        "        reconstruction_B = generator_AB(fake_A)\n",
        "        cycle_loss_B = cycle_loss(reconstruction_B, real_B)\n",
        "\n",
        "        overall_cycle_loss = (cycle_loss_A + cycle_loss_B) / 2\n",
        "\n",
        "        # Overall loss\n",
        "        g_loss = adv_loss + 10 * overall_cycle_loss + 5 * idn_loss\n",
        "\n",
        "\n",
        "        g_loss.backward()\n",
        "        optimizer_G.step()\n",
        "\n",
        "        #  Train Discriminator A\n",
        "        optimizer_D_A.zero_grad()\n",
        "\n",
        "        pred_real_A = discriminator_A(real_A)\n",
        "        loss_real_A = adversarial_loss(pred_real_A, valid)\n",
        "        pred_fake_A = discriminator_A(fake_A.detach())\n",
        "        loss_fake_A = adversarial_loss(pred_fake_A, fake)\n",
        "\n",
        "        # Discriminator_A loss\n",
        "        d_loss_A = 0.5 * (loss_real_A + loss_fake_A)\n",
        "\n",
        "        d_loss_A.backward()\n",
        "        optimizer_D_A.step()\n",
        "\n",
        "        #  Train Discriminator B\n",
        "        optimizer_D_B.zero_grad()\n",
        "\n",
        "        pred_real_B = discriminator_B(real_B)\n",
        "        loss_real_B = adversarial_loss(pred_real_B, valid)\n",
        "        pred_fake_B = discriminator_B(fake_B.detach())\n",
        "        loss_fake_B = adversarial_loss(pred_fake_B, fake)\n",
        "\n",
        "        # Discriminator_A loss\n",
        "        d_loss_B = 0.5 * (loss_real_B + loss_fake_B)\n",
        "\n",
        "        d_loss_B.backward()\n",
        "        optimizer_D_B.step()\n",
        "\n",
        "        ## Overall Discriminator Loss\n",
        "        d_loss = (d_loss_A + d_loss_B) / 2\n",
        "\n",
        "        # Progress Report\n",
        "        batches_done = epoch * len(train_dataloader) + i\n",
        "        print(f'Epoch: {epoch}/{N_EPOCHS}-Batch: {i}/{len(train_dataloader)}--D.loss:{d_loss.item():.4f},G.loss:{g_loss.item():.4f}--Adv.Loss:{adv_loss.item():.4f}')\n",
        "\n",
        "        # generate samples\n",
        "        if batches_done % SAMPLE_INTERVAL == 0:\n",
        "            sample_images(batches_done)"
      ],
      "metadata": {
        "id": "41d5MJCGZoZv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e28d9d65-6ac0-4c78-f199-d1c91413618c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-d6ae4b48b005>:9: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n",
            "  valid = Variable(Tensor(np.ones((real_A.size(0), *patch_gan_shape))), requires_grad=False)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0/200-Batch: 0/393--D.loss:0.7753,G.loss:9.0990--Adv.Loss:1.4075\n",
            "Epoch: 0/200-Batch: 1/393--D.loss:0.6744,G.loss:7.5540--Adv.Loss:0.5799\n",
            "Epoch: 0/200-Batch: 2/393--D.loss:1.1776,G.loss:7.1868--Adv.Loss:0.6048\n",
            "Epoch: 0/200-Batch: 3/393--D.loss:0.9952,G.loss:6.9940--Adv.Loss:0.7873\n",
            "Epoch: 0/200-Batch: 4/393--D.loss:0.4806,G.loss:6.3515--Adv.Loss:0.4175\n",
            "Epoch: 0/200-Batch: 5/393--D.loss:0.4426,G.loss:5.9172--Adv.Loss:0.5024\n",
            "Epoch: 0/200-Batch: 6/393--D.loss:0.3651,G.loss:5.3290--Adv.Loss:0.4427\n",
            "Epoch: 0/200-Batch: 7/393--D.loss:0.3167,G.loss:5.1697--Adv.Loss:0.3929\n",
            "Epoch: 0/200-Batch: 8/393--D.loss:0.3060,G.loss:4.9910--Adv.Loss:0.3688\n",
            "Epoch: 0/200-Batch: 9/393--D.loss:0.3028,G.loss:4.6442--Adv.Loss:0.3414\n",
            "Epoch: 0/200-Batch: 10/393--D.loss:0.3018,G.loss:4.9163--Adv.Loss:0.3537\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-0hkUoZtQPz_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}